This　study　aimed　to　develop　a　two-step　nitrification　model　to　predict　variations　in　aeration　time　and　nitrite　accumulation　rate　(NAR)　under　fluctuating　operational　conditions　in　mainstream　partial　nitritation　(PN)　processes.　Lab-scale　sequencing　batch　reactors　(SBRs)　were　used　to　evaluate　the　ammonia　oxidation　rate　(AOR)　and　nitrite　oxidation　rate　(NOR)　under　different　solids　retention　times　(SRT)　(10,　15,　20,　30,　and　50　days)　and　total　volumetric　nitrogen　loadings　(TVNL)　(20-60　mg　N/L　per　cycle).　A　static　model　was　developed　to　predict　consistent　AOR　and　NOR　values　in　the　steady　state,　whereas　a　dynamic　model　was　established　to　capture　the　growth　dynamics　of　ammonia-oxidizing　bacteria　(AOB)　and　nitrite-oxidizing　bacteria　(NOB)　under　unsteady-　state　conditions.　The　static　model　accurately　predicted　the　AOR,　NOR,　and　aeration　time　at　steady　state.　The　dynamic　model　quantified　the　relationship　between　specific　growth　rates　(mu)　and　food-to-microorganism　ratios　(F/M)　through　exponential　fitting,　successfully　capturing　AOB　and　NOB　growth　dynamics.　Validation　experiments　(SRT　=　10　d,　TVNL　=　60　mg/L　per　cycle)　demonstrated　the　ability　of　the　dynamic　model　to　predict　trends　in　NAR　and　aeration　time　accurately.　This　study　emphasizes　the　importance　of　accurately　modeling　AOR　and　NOR　variations　to　predict　aeration　time　and　NAR,　thereby　providing　valuable　insights　for　aeration　control　and　precise　management　of　AOB　and　NOB　populations　in　mainstream　PN　processes.